.ipynb .pdf

repository open issue suggest edit

Binder

On this page

Gulf Stream Currents

---

Overview

An example that uses ipyleaflet to reproduce the style of visualization used in the New York Times article In the Atlantic Ocean, Subtle Shifts Hint at Dramatic Dangers (March 2, 2021).

1. Open reference sea surface height data

2. Make a geographic map of the data using ipyleaflet

Prerequisites

Concepts	Importance	Notes
Xarray	Helpful
Dask	Helpful
Intake	Helpful
ipyleaflet	Helpful

• Time to learn: 15 minutes

Imports

---

from ipyleaflet import Map, TileLayer, basemaps
from ipyleaflet.velocity import Velocity
import xarray as xr

Load Data

The Copernicus Monitoring Environment Marine Service (CMEMS) is a large repository of ocean products including in-situ observations, satellite based remote sensing data, and numerical model output.

We want to look at altimeter satellite data to show the Sea Level Anomalies (SLA) for the global ocean. The particular data product is called Global Ocean Gridded L4 Sea Surface Heights and Derived Variables Reprocessed (1993-Ongoing) (SEALEVEL_GLO_PHY_L4_MY_008_047).

ds = xr.open_dataset("https://ncsa.osn.xsede.org/Pangeo/pangeo-cmems-duacs", consolidated=True, chunks='auto', engine="zarr")
ds

<xarray.Dataset>
Dimensions:    (time: 8901, latitude: 720, longitude: 1440, nv: 2)
Coordinates:
    crs        int32 ...
    lat_bnds   (time, latitude, nv) float32 dask.array<chunksize=(8901, 720, 2), meta=np.ndarray>
  * latitude   (latitude) float32 -89.88 -89.62 -89.38 ... 89.38 89.62 89.88
    lon_bnds   (longitude, nv) float32 dask.array<chunksize=(1440, 2), meta=np.ndarray>
  * longitude  (longitude) float32 0.125 0.375 0.625 0.875 ... 359.4 359.6 359.9
  * nv         (nv) int32 0 1
  * time       (time) datetime64[ns] 1993-01-01 1993-01-02 ... 2017-05-15
Data variables:
    adt        (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    err        (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    sla        (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    ugos       (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    ugosa      (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    vgos       (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
    vgosa      (time, latitude, longitude) float64 dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>
Attributes: (12/43)
    Conventions:                     CF-1.6
    Metadata_Conventions:            Unidata Dataset Discovery v1.0
    cdm_data_type:                   Grid
    comment:                         Sea Surface Height measured by Altimetry...
    contact:                         servicedesk.cmems@mercator-ocean.eu
    creator_email:                   servicedesk.cmems@mercator-ocean.eu
    ...                              ...
    summary:                         SSALTO/DUACS Delayed-Time Level-4 sea su...
    time_coverage_duration:          P1D
    time_coverage_end:               1993-01-01T12:00:00Z
    time_coverage_resolution:        P1D
    time_coverage_start:             1992-12-31T12:00:00Z
    title:                           DT merged all satellites Global Ocean Gr...

xarray.Dataset

• Dimensions:
  • time: 8901
  • latitude: 720
  • longitude: 1440
  • nv: 2
• Coordinates: (7)
  • crs
    ()
    int32
    ...

    comment :

    This is a container variable that describes the grid_mapping used by the data in this file. This variable does not contain any data; only information about the geographic coordinate system.

    grid_mapping_name :

    latitude_longitude

    inverse_flattening :

    298.257

    semi_major_axis :

    6378136.3

    [1 values with dtype=int32]

  • lat_bnds
    (time, latitude, nv)
    float32
    dask.array<chunksize=(8901, 720, 2), meta=np.ndarray>

    comment :

    latitude values at the north and south bounds of each pixel.

    units :

    degrees_north

    Array Chunk Bytes 48.89 MiB 48.89 MiB Shape (8901, 720, 2) (8901, 720, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • latitude
    (latitude)
    float32
    -89.88 -89.62 ... 89.62 89.88

    axis :

    Y

    bounds :

    lat_bnds

    long_name :

    Latitude

    standard_name :

    latitude

    units :

    degrees_north

    valid_max :

    89.875

    valid_min :

    -89.875

    array([-89.875, -89.625, -89.375, ...,  89.375,  89.625,  89.875],
          dtype=float32)

  • lon_bnds
    (longitude, nv)
    float32
    dask.array<chunksize=(1440, 2), meta=np.ndarray>

    comment :

    longitude values at the west and east bounds of each pixel.

    units :

    degrees_east

    Array Chunk Bytes 11.25 kiB 11.25 kiB Shape (1440, 2) (1440, 2) Dask graph 1 chunks in 2 graph layers Data type float32 numpy.ndarray

  • longitude
    (longitude)
    float32
    0.125 0.375 0.625 ... 359.6 359.9

    axis :

    X

    bounds :

    lon_bnds

    long_name :

    Longitude

    standard_name :

    longitude

    units :

    degrees_east

    valid_max :

    359.875

    valid_min :

    0.125

    array([1.25000e-01, 3.75000e-01, 6.25000e-01, ..., 3.59375e+02, 3.59625e+02,
           3.59875e+02], dtype=float32)

  • nv
    (nv)
    int32
    0 1

    comment :

    Vertex

    units :

    1

    array([0, 1], dtype=int32)

  • time
    (time)
    datetime64[ns]
    1993-01-01 ... 2017-05-15

    axis :

    T

    long_name :

    Time

    standard_name :

    time

    array(['1993-01-01T00:00:00.000000000', '1993-01-02T00:00:00.000000000',
           '1993-01-03T00:00:00.000000000', ..., '2017-05-13T00:00:00.000000000',
           '2017-05-14T00:00:00.000000000', '2017-05-15T00:00:00.000000000'],
          dtype='datetime64[ns]')

• Data variables: (7)
  • adt
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    comment :

    The absolute dynamic topography is the sea surface height above geoid; the adt is obtained as follows: adt=sla+mdt where mdt is the mean dynamic topography; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Absolute dynamic topography

    standard_name :

    sea_surface_height_above_geoid

    units :

    m

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • err
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    comment :

    The formal mapping error represents a purely theoretical mapping error. It mainly traduces errors induced by the constellation sampling capability and consistency with the spatial/temporal scales considered, as described in Le Traon et al (1998) or Ducet et al (2000)

    grid_mapping :

    crs

    long_name :

    Formal mapping error

    units :

    m

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • sla
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    comment :

    The sea level anomaly is the sea surface height above mean sea surface; it is referenced to the [1993, 2012] period; see the product user manual for details

    grid_mapping :

    crs

    long_name :

    Sea level anomaly

    standard_name :

    sea_surface_height_above_sea_level

    units :

    m

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ugos
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Absolute geostrophic velocity: zonal component

    standard_name :

    surface_geostrophic_eastward_sea_water_velocity

    units :

    m/s

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • ugosa
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    comment :

    The geostrophic velocity anomalies are referenced to the [1993, 2012] period

    grid_mapping :

    crs

    long_name :

    Geostrophic velocity anomalies: zonal component

    standard_name :

    surface_geostrophic_eastward_sea_water_velocity_assuming_sea_level_for_geoid

    units :

    m/s

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vgos
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    grid_mapping :

    crs

    long_name :

    Absolute geostrophic velocity: meridian component

    standard_name :

    surface_geostrophic_northward_sea_water_velocity

    units :

    m/s

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

  • vgosa
    (time, latitude, longitude)
    float64
    dask.array<chunksize=(15, 720, 1440), meta=np.ndarray>

    comment :

    The geostrophic velocity anomalies are referenced to the [1993, 2012] period

    grid_mapping :

    crs

    long_name :

    Geostrophic velocity anomalies: meridian component

    standard_name :

    surface_geostrophic_northward_sea_water_velocity_assuming_sea_level_for_geoid

    units :

    m/s

    Array Chunk Bytes 68.76 GiB 118.65 MiB Shape (8901, 720, 1440) (15, 720, 1440) Dask graph 594 chunks in 2 graph layers Data type float64 numpy.ndarray

• Indexes: (4)
  • latitude
    PandasIndex

    PandasIndex(Index([-89.875, -89.625, -89.375, -89.125, -88.875, -88.625, -88.375, -88.125,
           -87.875, -87.625,
           ...
            87.625,  87.875,  88.125,  88.375,  88.625,  88.875,  89.125,  89.375,
            89.625,  89.875],
          dtype='float32', name='latitude', length=720))

  • longitude
    PandasIndex

    PandasIndex(Index([  0.125,   0.375,   0.625,   0.875,   1.125,   1.375,   1.625,   1.875,
             2.125,   2.375,
           ...
           357.625, 357.875, 358.125, 358.375, 358.625, 358.875, 359.125, 359.375,
           359.625, 359.875],
          dtype='float32', name='longitude', length=1440))

  • nv
    PandasIndex

    PandasIndex(Index([0, 1], dtype='int32', name='nv'))

  • time
    PandasIndex

    PandasIndex(DatetimeIndex(['1993-01-01', '1993-01-02', '1993-01-03', '1993-01-04',
                   '1993-01-05', '1993-01-06', '1993-01-07', '1993-01-08',
                   '1993-01-09', '1993-01-10',
                   ...
                   '2017-05-06', '2017-05-07', '2017-05-08', '2017-05-09',
                   '2017-05-10', '2017-05-11', '2017-05-12', '2017-05-13',
                   '2017-05-14', '2017-05-15'],
                  dtype='datetime64[ns]', name='time', length=8901, freq=None))

• Attributes: (43)

  Conventions :

  CF-1.6

  Metadata_Conventions :

  Unidata Dataset Discovery v1.0

  cdm_data_type :

  Grid

  comment :

  Sea Surface Height measured by Altimetry and derived variables

  contact :

  servicedesk.cmems@mercator-ocean.eu

  creator_email :

  servicedesk.cmems@mercator-ocean.eu

  creator_name :

  CMEMS - Sea Level Thematic Assembly Center

  creator_url :

  http://marine.copernicus.eu

  date_created :

  2014-02-26T16:09:13Z

  date_issued :

  2014-01-06T00:00:00Z

  date_modified :

  2015-11-10T19:42:51Z

  geospatial_lat_max :

  89.875

  geospatial_lat_min :

  -89.875

  geospatial_lat_resolution :

  0.25

  geospatial_lat_units :

  degrees_north

  geospatial_lon_max :

  359.875

  geospatial_lon_min :

  0.125

  geospatial_lon_resolution :

  0.25

  geospatial_lon_units :

  degrees_east

  geospatial_vertical_max :

  0.0

  geospatial_vertical_min :

  0.0

  geospatial_vertical_positive :

  down

  geospatial_vertical_resolution :

  point

  geospatial_vertical_units :

  m

  history :

  2014-02-26T16:09:13Z: created by DUACS DT - 2015-11-10T19:42:51Z: Change of some attributes - 2017-01-06 12:12:12Z: New format for CMEMSv3

  institution :

  CLS, CNES

  keywords :

  Oceans > Ocean Topography > Sea Surface Height

  keywords_vocabulary :

  NetCDF COARDS Climate and Forecast Standard Names

  license :

  http://marine.copernicus.eu/web/27-service-commitments-and-licence.php

  platform :

  ERS-1, Topex/Poseidon

  processing_level :

  L4

  product_version :

  5.0

  project :

  COPERNICUS MARINE ENVIRONMENT MONITORING SERVICE (CMEMS)

  references :

  http://marine.copernicus.eu

  source :

  Altimetry measurements

  ssalto_duacs_comment :

  The reference mission used for the altimeter inter-calibration processing is Topex/Poseidon between 1993-01-01 and 2002-04-23, Jason-1 between 2002-04-24 and 2008-10-18, OSTM/Jason-2 since 2008-10-19.

  standard_name_vocabulary :

  NetCDF Climate and Forecast (CF) Metadata Convention Standard Name Table v37

  summary :

  SSALTO/DUACS Delayed-Time Level-4 sea surface height and derived variables measured by multi-satellite altimetry observations over Global Ocean.

  time_coverage_duration :

  P1D

  time_coverage_end :

  1993-01-01T12:00:00Z

  time_coverage_resolution :

  P1D

  time_coverage_start :

  1992-12-31T12:00:00Z

  title :

  DT merged all satellites Global Ocean Gridded SSALTO/DUACS Sea Surface Height L4 product and derived variables

Make a Map

center = [35, -50]
zoom = 4
m = Map(center=center, zoom=zoom, interpolation='nearest', basemap=basemaps.Gaode.Satellite)

display_options = {
    'velocityType': 'Global Wind',
    'displayPosition': 'bottomleft',
    'displayEmptyString': 'No wind data'
}

wind = Velocity(
    data=ds.isel(time=-1), 
    zonal_speed='ugos', meridional_speed='vgos', 
    latitude_dimension='latitude', longitude_dimension='longitude', 
    velocity_scale=0.2, max_velocity=1, 
    display_options=display_options
)

m.add_layer(wind)

m

---

Summary

In this example we loaded sea level data from an analysis-ready cloud based dataset and made a visualization of that data using ipyleaflets.

Resources and references

• This notebook is based on the Pangeo physical oceanography gallery example: https://gallery.pangeo.io/repos/pangeo-gallery/physical-oceanography/05_gulf_stream_currents.html

previous

MITgcm ECCOv4 Example